The Australian Research Data Commons (ARDC) invites you to participate in a short survey about your
interaction with the ARDC and use of our national research infrastructure and services. The survey will take
approximately 5 minutes and is anonymous. It’s open to anyone who uses our digital research infrastructure
services including Reasearch Link Australia.
We will use the information you provide to improve the national research infrastructure and services we
deliver and to report on user satisfaction to the Australian Government’s National Collaborative Research
Infrastructure Strategy (NCRIS) program.
Please take a few minutes to provide your input. The survey closes COB Friday 29 May 2026.
Complete the 5 min survey now by clicking on the link below.
Mixture models for high-dimensional clustering with applications to tumour classification, network intrusion, and text classification. This project will benefit the Australian Society as a whole by developing statistical methodology for the clustering of high-dimensional data. In particular, it will develop a novel and efficient model for extracting useful information from subpopulations. It thus has wide applicability to improving the quality and validity of applied research in most industries ....Mixture models for high-dimensional clustering with applications to tumour classification, network intrusion, and text classification. This project will benefit the Australian Society as a whole by developing statistical methodology for the clustering of high-dimensional data. In particular, it will develop a novel and efficient model for extracting useful information from subpopulations. It thus has wide applicability to improving the quality and validity of applied research in most industries in Australia. More specifically, it is to be applied here to classify brain tumours and detect network intruders. This cross-disciplinary project will contribute to Australia's economic of public health, protect Australia from crime, and strength Australian researchers' capacity and capability of participating in this emerging science.Read moreRead less
Novel Applied Bayesian Statistics for Monitoring Neuromuscular Diseases. Neurological diseases such as motor neurone disease are caused by the progressive death of motor units serving a muscle. Currently there are no ways of quantifying and detecting change in the number of motor units serving a muscle which are non-invasive. Our research will provide an objective method for the progression of neuromuscular diseases to be monitored with minimal inconvenience to patients. This will allow clinic ....Novel Applied Bayesian Statistics for Monitoring Neuromuscular Diseases. Neurological diseases such as motor neurone disease are caused by the progressive death of motor units serving a muscle. Currently there are no ways of quantifying and detecting change in the number of motor units serving a muscle which are non-invasive. Our research will provide an objective method for the progression of neuromuscular diseases to be monitored with minimal inconvenience to patients. This will allow clinical trials for possible effective treatments of neurological diseases such as motor neurone disease to be conducted with an objective measurement of disease progression. Read moreRead less
Motor Unit Numbers Estimation (MUNE) using Bayesian statistical methodology for monitoring of progression of neuromuscular diseases. A means of objectively measuring the pathology of a neuromuscular disease involving motor unit loss, such as motor neuron disease, is much needed. This will be achieved by using newly developed electrophysiological techniques and developing new Bayesian statistical methodology to determine the number of motor units that supply a muscle. Our innovations will reliabl ....Motor Unit Numbers Estimation (MUNE) using Bayesian statistical methodology for monitoring of progression of neuromuscular diseases. A means of objectively measuring the pathology of a neuromuscular disease involving motor unit loss, such as motor neuron disease, is much needed. This will be achieved by using newly developed electrophysiological techniques and developing new Bayesian statistical methodology to determine the number of motor units that supply a muscle. Our innovations will reliably determine the number of motor units that supply a muscle in both normal subjects and in diseased patients with loss of motor nerves. This will enable the monitoring of disease progression. An outcome will be a software package that can be used with standard electrophysiology machines.Read moreRead less